GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
Abstract
We investigate the potential implications of Generative Pre-trained Transformer (GPT) models and related technologies on the U.S. labor market. Using a new rubric, we assess occupations based on their correspondence with GPT capabilities, incorporating both human expertise and classifications from GPT-4. Our findings indicate that approximately 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of GPTs, while around 19% of workers may see at least 50% of their tasks impacted. The influence spans all wage levels, with higher-income jobs potentially facing greater exposure. Notably, the impact is not limited to industries with higher recent productivity growth. We conclude that Generative Pre-trained Transformers exhibit characteristics of general-purpose technologies (GPTs), suggesting that as these models could have notable economic, social, and policy implications.
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I love how they're talking about impacted "tasks" as a way to disconnect themselves for the redundancies that may happen as a consequence. The more serious impacts of GPT-4, like job displacement, are left for future research.
In order to keep up with the 2 GPTs pun they made in the title, the paper got bit confusing to read IMO
Thought I think the concept of exposure is interesting
We define exposure as a proxy for potential economic impact without distinguishing between labor-augmenting or labor-displacing effects.
However, one thing that is not clear from the paper to me is, in this part:
To ensure the quality of these annotations, the authors personally labeled a large sample of tasks and DWAs and enlisted experienced human annotators who have extensively reviewed GPT outputs as part of OpenAI’s alignment work (Ouyang et al., 2022).
Why would someone that reviewed a lot of GPT outputs be qualified to assess which industries it can affect the most, without experts on each industry involved? But if I understood it correctly, I guess they try to validate that with the fact that GPT-4 agrees in most part with the human labels
To me the most interesting chart of the paper is this one:
(was flipped on the paper and a bit low quality to read, but interesting to see which industries ranked high on exposure by the human annotators)
L
To me the most interesting chart of the paper is this one:
(was flipped on the paper and a bit low quality to read, but interesting to see which industries ranked high on exposure by the human annotators)
The list seems to be quite randomly ordered; Telecommunications is separated from Utilities by quite a bit - but they are similar industries in many ways (have to have a lot of folks digging lots of holes in the road).
Also I see barbers as being up to 50% exposed (Table 6) and Mathematicians 100% (Table 4) - my limited understanding of both those jobs makes me think that they are actually 0% exposed.
And I thought I would live this moment in 20 years. This is so exciting...
"Securities, commodities and financial investments"... yeah as if governments wont regulate the use of these technologies in this space (especially bond markets) in a heartbeat..
Don't think that these wild AIs wont be regulated -possibly banned entirely- for large banks and other critical institutions...
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